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Improving portfolio performance of renewable energy stocks using robust portfolio approach: Evidence from China

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  • Bai, Lan
  • Liu, Yuntong
  • Wang, Qian
  • Chen, Chen

Abstract

As the largest coal and the second largest crude oil consumer in the world, China has urgent task to develop its renewable energy industry quickly. By now, there are over 80 renewable energy companies listed in China’s stock exchanges. Thus to manage the market risk and achieve better investment returns of renewable energy stocks in China is of great value for policy makers and investors. The aim of this paper is to introduce an innovative portfolio allocation approach, robust portfolio, to improve portfolio performance of renewable energy stocks in China by considering the parameter uncertainty in the process of portfolio optimization. Furthermore, to make the conclusions more robust, we classify the stock market into three commonly recognized statuses: bull market, bear market and steady market, respectively, and compare the performances of robust portfolio method with traditional Markowitz approach. The empirical results indicate that the robust portfolio method can produce better performance of the renewable energy stock portfolio than Markowitz approach in various market statuses with much more flexibility in handling the problem of parameter uncertainty. This paper provides an alternative but very effective strategy other than Markowitz method for the portfolio allocation of renewable energy stocks in China.

Suggested Citation

  • Bai, Lan & Liu, Yuntong & Wang, Qian & Chen, Chen, 2019. "Improving portfolio performance of renewable energy stocks using robust portfolio approach: Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    • Handle: RePEc:eee:phsmap:v:533:y:2019:i:c:s0378437119311562
    DOI: 10.1016/j.physa.2019.122059
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    Citations

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    Cited by:

    1. Alireza Ghahtarani & Ahmed Saif & Alireza Ghasemi, 2022. "Robust portfolio selection problems: a comprehensive review," Operational Research, Springer, vol. 22(4), pages 3203-3264, September.
    2. Roy, Preeti & Ahmad, Wasim & Sadorsky, Perry & Phani, B.V., 2022. "What do we know about the idiosyncratic risk of clean energy equities?," Energy Economics, Elsevier, vol. 112(C).
    3. José Luis Miralles-Quirós & María Mar Miralles-Quirós, 2021. "Alternative Financial Methods for Improving the Investment in Renewable Energy Companies," Mathematics, MDPI, vol. 9(9), pages 1-25, May.
    4. Mazin A.M. Al Janabi, 2021. "Is optimum always optimal? A revisit of the mean‐variance method under nonlinear measures of dependence and non‐normal liquidity constraints," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 387-415, April.
    5. José Alex Gualotuña Parra & Omar Valverde-Arias & Ana M. Tarquis & Juan B. Grau Olivé & Federico Colombo Speroni & Antonio Saa-Requejo, 2023. "Combining Markowitz Portfolio Model and Simplex Algorithm to Achieve Sustainable Land Management Objectives: Case Study of Rivadavia Banda Norte, Salta (Argentina)," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    6. Alireza Ghahtarani & Ahmed Saif & Alireza Ghasemi, 2021. "Robust Portfolio Selection Problems: A Comprehensive Review," Papers 2103.13806, arXiv.org, revised Jan 2022.
    7. Wang, Yilin & Zhang, Zeming & Li, Xiafei & Chen, Xiaodan & Wei, Yu, 2020. "Dynamic return connectedness across global commodity futures markets: Evidence from time and frequency domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    8. Hemrit, Wael & Benlagha, Noureddine, 2021. "Does renewable energy index respond to the pandemic uncertainty?," Renewable Energy, Elsevier, vol. 177(C), pages 336-347.

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